StockTrainer: Stock Environment for Human
StockTrainer: Stocks Made Easy
StockTrainer is high level API data generator for training python machine learning models on stock/cryptocurrency data and is capable of running with Keras, Tensorflow, sklearn, and many other machine learning APIs
Capabilities:
- Predict day to day stock prices
- Use multiple days to predict next stock price
- Predict succeeding stock prices over multiple days
- Train a reinforcement learning agent to simulate stock trades
Documentation available soon ;)
StockTrainer is compatible with: Python 3.6+
Getting Started
The core of algorithm is the model, here is a simple LSTM model to based on 5 days of stock data to predict the next
import keras
import numpy as np
from keras.models import Sequential
from keras.layers import Dropout ,BatchNormalization, LSTM, Dense
model = Sequential()
#input shape 5 days of data
#each day has 6 data points (open, close, high , low volums, adj CLose)
model.add(BatchNormalization(input_shape=(5, 6)))#batchnorm bc high values
model.add(LSTM(512, return_sequences=True, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='relu'))
model.compile(loss='mse', optimizer='adam')
Next import StockTrainer and create your environment
from StockTrainer import Env
enviorment = Env("Standard", "AAPL")
Time to collect your data to train!!!
test_percent =.30
shuffle =True
start_date ='2003-01-01'
end_date='now'
agent_memory = 5
seed = 42
trainx,testx,trainy, testy = environment.train_test(
test_percent= test_percent, shuffle = shuffle,
start_date=start_date, end_date=end_date,
agent_memory=agent_memory, seed=seed)
Futher information on parameters in Documentation
That's it now train and test your model
#fit model
model.fit(trainx, trainy, epochs=10, batch_size=128, verbose=2)
model.save('model.h5')
#evaluate model
model.evaluate(testx,testy )
#use model to predict
model.predict(testx)
More examples on samples folder in github
Installation
Using pip
pip install StockEnv
or download directly: https://pypi.org/project/StockEnv/
